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Machine learning powered framework for detection of micro- and nanoplastics using optical photothermal infrared spectroscopy
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A machine learning framework was developed to detect and classify micro- and nanoplastics using optical photothermal infrared spectroscopy, addressing the lack of standardized detection methods in the field. The approach improves accuracy and consistency in identifying plastic particles, potentially enabling better monitoring of environmental and human health risks.
Despite the breadth of scientific literature on micro- and nanoplastics (MNPs), a standardized procedure for detecting MNPs is still lacking so far, leading to incomparable results between published studies. This work innovatively proposed the combination of machine learning with advanced optical photothermal infrared (O-PTIR) spectroscopy to develop an efficient and reliable detection framework for MNPs. Spectra of MPs and non-MPs were first collected and inputted to build a classification model, based on which four important wavenumbers were selected. A simplified support vector machine (SVM) model was subsequently developed using the selected four wavenumbers. Good predictive ability was evidenced by a high accuracy of 0.9133. The developed method can improve speed as well as the reliability of results, having a great potential for routine analysis of MNPs, ultimately leading to the standardization of detection methods.
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